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arXiv · 2605.23188

SpikingMoE: SDPrompt-Guided Dynamic Expert Fusion in Spiking Neural Networks

Yukai Yang·Chenxi Qin·Jungang Li·Xin Zhang·Wenwei Shao·Liqun Chen
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Abstract

Spiking Neural Networks (SNNs) provide an energy-efficient paradigm for visual recognition. We present SpikingMoE, which integrates a spike-driven Transformer with a Mixture-of-Experts (MoE) framework for dynamic computation. Inspired by the lateral geniculate nucleus (LGN), a spike-driven prompt (SDprompt) enables input-dependent expert routing in a biologically plausible manner. By replacing standard MLPs with spike-compatible expert modules and enforcing binary spike communication, SpikingMoE is designed for neuromorphic hardware. Experiments on CIFAR-10 and CIFAR-100 achieve 94.09% and 74.54% top-1 accuracy, showing that modular expert routing can be incorporated while retaining reasonable performance. To our knowledge, SpikingMoE is the first open-source SNN framework that integrates MoE into a spike-driven Transformer with LGN-inspired routing.

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Yukai Yang, Chenxi Qin, Jungang Li, Xin Zhang, Wenwei Shao, Liqun Chen. 2026-05-22. SpikingMoE: SDPrompt-Guided Dynamic Expert Fusion in Spiking Neural Networks. https://arxiv.org/abs/2605.23188

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